Imperial College London

Academic Institute

Alison Holmes

Unknown

No

NIBDAPC_2025_0045

Nosocomial infection transmission happens when bugs which cause infections spread from one person to another within hospitals or during the process of getting medical care. This can happen between patients, from patients to visitors and vice versa, or from patients to healthcare workers and vice versa. Common nosocomial infection transmissions include the spread of COVID-19 or flu in hospitals. Nosocomial infections do not only cause patient suffering, they also make healthcare workers sick, and sometimes lead to the closure of hospital wards to stop further spreading. Nosocomial tranmissions are often caused by direct or indirect human contact, therefore, the data describing patient movement is useful to help understand how bugs spread. In this project, we have previously developed a mathematical model using patient movement data to capture how nosocomial COVID-19 spread within hospitals. The patient movement data from the ICHT-COVID database, including ward names and bed names, and admission and discharge date and time, was used to construct networks of patient contacts. These networks help visualise how infected cases potentially passed pathogens to the uninfected cases because they have been located in the same ward or have been in the beds next to each other. Then these modelled transmission events were confirmed using the actual laboratory data confirming SARS-CoV-2 infection status and the timing of infection onset. This work then has been directly implemented as a surveillance measure during the pandemic. We hope to carry on developing similar mathemetical models to monitor the spread of other types of bugs, including those could cause patient death if not prevented. The key pathogens with national and international priorities are Carbapenemase-Producing Enterobacterales (CPE), which can spread in hospitals and requires ward closure and patient isolation when it happens.

We believe this expanded / continued research will generate the following benefit: 1. Guide infection prevention and control (IPC) measures: the simulation of nosocmoal infection transmission, combined with the risk prediction model, will identify high-risk locations and patient cohorts to guide targeted IPC measures. 2. Protect healthcare workforces and pathoways: nosocomial transmission does not only affect patients, it also affects healthworkforce and the whole healthcare system. For instance, transmission of certain multi-drug resistant bacteria, such as Methicllin-resistant Staphyloccous auries (MRSA), or Carbapenemase-producing Enterobacterales (CPE), requires the closure of the whole medical ward. Nosocomial transmission of respiratory infections, such as COVID-19 or inflenza, leads to loss of medical workforce due to staff absence. This research generates evidence in infection transmission dynamics and the underlying risk factors, including both patient and healthcare charateristics, which can support designing interventions to protect healthcare workforces and pathoways. 3. Detect outbreaks: the network analysis based on patient movement will identify clusters of nosocomial cases, this provides a less time- and resource-consuming method to complement and potentially replace whole genomic sequencing. 4. Predict system demand: by simulating transmission events, this project provides evidence to support healthcare planning based on the demand, this is especially important during periods such as winter surges or outbreaks.

Public Health Research

11/04/2025

De-Personalised

Not applicable

Not applicable

One-off

TRE